code: def forward(self, x1, x2, x3):
      # x1.size = x2.size = x3.size = (N, S, S, c)
      # where N is the batch size
      size = x1.shape[-2]
      input_list = [x1, x2, x3]
      for m1, m2 in [(0, 1), (2, 0), (1, 2)]:
          matrix = torch.cat([input_list[m1], input_list[m2]], dim=-2)
          # matrix_size = (N, S, 2S, c)
          out = self.attention(matrix, matrix)
          input_list[m1] = out[:, :, :size]
          input_list[m2] = out[:, :, size:]
      return input_list
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/modules/torso.py
imports: import torch
  from open_alpha_tensor.core.modules.attention import AlphaMultiHeadAttention
label_desc: Use image node_id 2 for calling the TorsoAttentiveModes obj's underlying member method code with example usage: obj.forward(x1, x2, x3) and returns: input_list